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Implementing Multi Layer Perceptron In Pure C++ | Part 6
Building a MLP and Tensor Class: Step-by-Step Guide
In this tutorial, we continue our neural network series by constructing a layer class. Building on the foundation laid in our previous session where we developed a neuron class, we now turn our attention to how layers are composed of multiple neurons. The tutorial begins with an introduction to the neuron class and progresses into the specifics of building the layer class, where we explore initializing data members, defining constructors, and overloading operators to enhance functionality. Key additional functions, such as zeroing gradients and printing parameters, are also covered in detail. We conclude with a section dedicated to testing the layer class to ensure everything works as expected. By the end of the tutorial, you’ll have a solid understanding of layer construction, which sets the stage for the next lecture where we will build a complete neural network and prepare it for real-world applications.
github:
Code in lecture: github.com/ggs...
Completed code main repo: github.com/ggs...